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A 3D Data Transformation Processor

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A 3D Data Transformation Processor. Dimitrios Megas , Kleber Pizolato , Timothy Levin, and Ted Huffmire WESS 2012 October 11, 2012. Disclaimer. - PowerPoint PPT Presentation
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A 3D Data Transformation Processor Dimitrios Megas, Kleber Pizolato, Timothy Levin, and Ted Huffmire WESS 2012 October 11, 2012
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Page 1: A 3D Data Transformation Processor

A 3D Data Transformation Processor

Dimitrios Megas, Kleber Pizolato, Timothy Levin, and Ted Huffmire

WESS 2012October 11, 2012

Page 2: A 3D Data Transformation Processor

Disclaimer

• The views presented in this talk are those of the speaker and do not necessarily reflect the views of the United States Department of Defense or the National Science Foundation.

Page 3: A 3D Data Transformation Processor

Split Manufacturing

• Face-to-Back (F2B) Bonding

Page 4: A 3D Data Transformation Processor

Basic Idea

• Combine using 3D integration:– Processor– Compression coprocessor– Cryptographic coprocessor

Page 5: A 3D Data Transformation Processor

Basic Idea

• CPU Layer + Coprocessor Layer

Page 6: A 3D Data Transformation Processor

Basic Idea

• Real-time trace collection– Compress trace prior to transmission to off-chip storage

for offline program analysis• Optional encryption step can protect the compressed data

from interception– High-performance stand-alone encryption service– XTRec: Secure Real-time Execution Trace Recording on

Commodity Platforms (CMU)– Trusted computing: mitigate glitch attack against TPM

(runtime hash of memory, capture sequence of instructions executed)

Page 7: A 3D Data Transformation Processor

Basic Idea

• Real-time trace collection– The amount of data collected depends on the

granularity of the collection and the speed of the system

– Monitoring and collecting more signals results in a larger data stream

Page 8: A 3D Data Transformation Processor

Outline

• Motivation and Background• Design Goals• Design Choices• System Architecture• Conclusions and Future Work

Page 9: A 3D Data Transformation Processor

Outline

• Motivation and Background• Design Goals• Design Choices• System Architecture• Conclusions and Future Work

Page 10: A 3D Data Transformation Processor

Cryptographic Coprocessing

• 3D vs. 2D

Page 11: A 3D Data Transformation Processor

Medical Image Processing

• [Cong 2011]

Page 12: A 3D Data Transformation Processor

3D-MAPS V1 vs V2

• Georgia Tech [Kim et al., ISSCC 2012]3D-MAPS V1 3D-MAPS V2

# of tiers 2 (1 logic, 1 SRAM) 5 (2 logic, 3 DRAM)

# of cores 64 128Memory capacity 256KB SRAM 256MB DRAM & 512KB SRAM

Logic footprint 5mm X 5mm 10mm X 10mm

DRAM footprint - 20mm X 12mm

Bonding style F2F F2F and F2B

TSV/F2F usage ~ 50K / ~50K ~ 150K / ~185K

Memory access* 2048 bit/cycle SRAM 1024 bit/cycle DRAM

freq / power 277MHz / 4.0W 175MHz / 10.4W

* Wide-I/O allows 512 bit/cycle DRAM access

Page 13: A 3D Data Transformation Processor

Stack Up Comparison• TSV usage

– 3D-MAPS V1: For I/O (204 redundancy)– 3D-MAPS V2: For I/O (204 redundancy) and DRAM

access (9 redundancy)

Page 14: A 3D Data Transformation Processor

What is 3Dsec?

• Economics of High Assurance– High NRE Cost, Low Volume– Gap between DoD and Commercial

• Disentangle security from the COTS– Use a separate chip for security– Use 3-D Integration to combine:

• Control Plane• Computation Plane

– Need to add posts to the COTS chip design• Dual use of computation plane

Page 15: A 3D Data Transformation Processor

Pro’s and Con’s

• Why not use a co-processor? On-chip?• Pro’s

– High bandwidth and low latency– Controlled lineage– Direct access to internal structures

• Con’s– Thermal and cooling– Design and testing– Manufacturing yield

Page 16: A 3D Data Transformation Processor

Cost

• Cost of fabricating systems with 3-D– Fabricating and testing the security layer– Bonding it to the host layer– Fabricating the vias– Testing the joined unit

Page 17: A 3D Data Transformation Processor

Circuit-Level Modifications

• Passive vs. Active Monitoring– Tapping– Re-routing– Overriding– Disabling

Page 18: A 3D Data Transformation Processor

3-D Application Classes

• Enhancement of native functions• Secure alternate service• Isolation and protection• Passive monitoring

– Information flow tracking– Runtime correctness checks– Runtime security auditing

Page 19: A 3D Data Transformation Processor

Outline

• Motivation and Background• Design Goals• Design Choices• System Architecture• Conclusions and Future Work

Page 20: A 3D Data Transformation Processor

Design Goals

• High Performance• Ability to gather and compress architectural

state of a processor at runtime

Page 21: A 3D Data Transformation Processor

Outline

• Motivation and Background• Design Goals• Design Choices• System Architecture• Conclusions and Future Work

Page 22: A 3D Data Transformation Processor

Design Choices

• Manufacturing process– Face-to-face (F2F)

• Compression algorithm/hw– Two stages: filtering + general-purpose

• Crypto algorithm/hw– AES-128, SHA-1, SHA-512

• Interface between planes– 128 F2F vias up, 32 down (direct connection)

Page 23: A 3D Data Transformation Processor

Design Choices

• Other Issues– Coordination between planes

• Control words in special registers– Interface within control plane

• Output of compression input of crypto– Delivery of I/O and power

• Use existing capability of computation plane– Computation plane hardware

• High-performance general-purpose processor– Clock synchronization

• Tree network

Page 24: A 3D Data Transformation Processor

Compression Study

• Use TCgen to compress a set of trace files generated using Pin– Traces capture memory access behavior of various

Linux applications• Vary parameters of TCgen for each field

– TCgen is prediction-based compression– Which algorithm is most effective?

• Apply general-purpose compression in second stage (gzip)

Page 25: A 3D Data Transformation Processor

Trace Files (generated by Pin)• Instruction• CountPC ADDRESS Size• 8 0x52d70b 0x5913c0004• 25 0x543cc6 0xbff102544• 25 0x543cc7 0xbff102584• 33 0x52d6bb 0xbff1025c4• 33 0x52d6be 0xbff102604• 33 0x52d6c2 0xbff102644• 33 0x52d6c8 0xbff102684• 33 0x52d6c9 0xbff1026c4• 37 0x9bcb44 0xa1a508004• 40 0x6eb126 0xbff102684

Page 26: A 3D Data Transformation Processor

PC Field

• Number of correct predictions (%) for each configuration of TCgen when compressing the PC field (average of all 5 trace files)

Page 27: A 3D Data Transformation Processor

Data Address Field

• Number of correct predictions (%) for each configuration of TCgen when compressing address field (average of all 5 trace files)

Page 28: A 3D Data Transformation Processor

PC Field

• Compression ratio for the PC field

Page 29: A 3D Data Transformation Processor

Data Address Field

• Compression ratio for the data address field

Page 30: A 3D Data Transformation Processor

Outline

• Motivation and Background• Design Goals• Design Choices• System Architecture• Conclusions and Future Work

Page 31: A 3D Data Transformation Processor

Computation Plane

• CPU

Page 32: A 3D Data Transformation Processor

Control Plane

• Compression coprocessor (DFCM + gzip)

Page 33: A 3D Data Transformation Processor

Control Plane

• gzip unit (within compression coprocessor)

Page 34: A 3D Data Transformation Processor

Control Plane

• AES/SHA

Page 35: A 3D Data Transformation Processor

Control Plane

• Microprocessor interface unit

Page 36: A 3D Data Transformation Processor

Full 3D System

• 3D IC

Page 37: A 3D Data Transformation Processor

Outline

• Motivation and Background• Design Goals• Design Choices• System Architecture• Conclusions and Future Work

Page 38: A 3D Data Transformation Processor

Conclusions• Applications: trusted computing, reverse engineering of

malicious software, post-mortem analysis of system that has suffered an attack

• Simple preprocessing can decrease bandwidth (also gives power advantages)

• There is much to do before making silicon. It is useful to quantify the high-level tradeoffs:– Data to compress– Sampling rate– Number of TSVs– Throughput

Page 39: A 3D Data Transformation Processor

Future Work

• Independent I/O and power delivery– How to share the I/O of computation plane?

• Floor Planning– How much logic/memory can you fit between the TSVs?

• It would be helpful for the 3D chip to be pin-compatible with the 2D package.– Use a network/share the TSVs?

• Joining dissimilar technology nodes– Use buffers, redundant hardware

Page 40: A 3D Data Transformation Processor

Future Work• More types of trace files

– General-purpose interface, migration path– Can you test/verify computation plane without knowing what the control plane will be?– Characteristics of a “typical” trace file?

• Hierarchy of compression, for power not just for compression ratio?– Lossy compression?!

• Trust issues– Who generates the write signal?– How to protect the key?– Can monitored software turn off monitoring?

• Hardware implementation– Simulation– FPGA prototype– Tape-out

Page 41: A 3D Data Transformation Processor

Split Manufacturing

• Discussion Points– Can we trust the result of split manufacturing?– Could this approach harm security?– Is it worth it? When is it worth it?– Why not use trusted foundry always?– Are trusted foundries a band aid solution to offshoring

trend?– How to trust trusted foundry?– Why not use redundancy with majority vote?– Can we do everything from scratch?

Page 42: A 3D Data Transformation Processor

Split Manufacturing

• Discussion Points– How to raise alarm if network interface is

controlled by adversary?• Use challenge-response protocols?

– Security architecture• Packaging considerations

• Distributed posts, policy state?• If computation plane can perform AES, why

perform AES in control plane?

Page 43: A 3D Data Transformation Processor

Questions?

• faculty.nps.edu/tdhuffmi


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